Molecule‐based devices are envisioned to complement silicon devices by providing new functions or by implementing existing functions at a simpler process level and lower cost, by virtue of their self‐organization capabilities. Moreover, they are not bound to von Neuman architecture and this feature may open the way to other architectural paradigms. Neuromorphic electronics is one of them. Here, a device made of molecules and nanoparticles—a nanoparticle organic memory field‐effect transistor (NOMFET)—that exhibits the main behavior of a biological spiking synapse is demonstrated. Facilitating and depressing synaptic behaviors can be reproduced by the NOMFET and can be programmed. The synaptic plasticity for real‐time computing is evidenced and described by a simple model. These results open the way to rate‐coding utilization of the NOMFET in dynamical neuromorphic computing circuits.

Grant et al, [2] Markram et al, [3] Bi, and Poo [4] observed STDP in biological synapses. The principle of STDP is to tune the response of a synapse as a function of the pre-and post-synaptic neurons spiking activity - Figure 1a. Depending on the correlation or anti-correlation of the spiking events of the pre-and postsynaptic neurons, the synapse's weight is reinforced or depressed, respectively. The so-called "STDP function" or "STDP learning window" is defined as the relationship between the change in the synaptic weight or synaptic response versus the relative timing between the pre-and postsynaptic spikes (Figure 1b). [5] The implementation of STDP with nanodevices is strongly driven by a bio-inspired approach to enable local and unsupervised learning capability in large artificial SNN in an efficient and robust way. To this end, it is envisioned to use the nanodevices as synapses and to realize the neuron functionality with complementary metal oxide semiconductor (CMOS) technology. This approach is supported by the fact that the limiting integration factor is really the synapse density, as realistic applications could require as much as 10 3 to 10 4 synapses per neuron. Snider [6] proposed an implementation of STDP with nanodevices, where the synapses are realized with a crossbar of memristors [7] and the neurons with a "time-multiplexing CMOS" circuit. Using these two elements, it should be possible to reproduce exactly the "STDP learning window" of a biological synapse (Figure 1b). Linares-Barranco et al. simulated the implementation of the STDP function with memristive nanodevices. [8,9] Using a specific shape of the spikes and the nonlinearity of the memristor, they showed that the conductivity of the memristor can be tuned depending on the precise timing between the postsynaptic and presynaptic spikes. More interestingly, they showed that the shape of the STDP learning window can be tuned by changing the shape of the spike (Figure 1c). We have to emphasize that our aim is to be inspired by the behavior of a biological synapse for neural computation applications (and not to build a model system of the synapse), thus the important point is to reproduce qualitatively the STDP behavior, even if the spike signals applied to the synapstor are not close to the real biological spike. A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired ComputingA large effort is devoted to the research of new computing paradigms associated with innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS (complementary metal oxide semiconductor) association. Among various propositions, spiking neural network (SNN) seems a valid candidate. i) In terms of functions, SNN using relative spike timing for information coding are deemed to be the most effective at taking inspiration from the brain to allow fast and efficient processing of information for complex tasks in recognition or classification. ii) In terms of technology, SNN may be able to benefit the...

In this letter, we present an original demonstration of an associative learning neural network inspired by the famous Pavlov's dogs experiment. A single nanoparticle organic memory field effect transistor (NOMFET) is used to implement each synapse. We show how the physical properties of this dynamic memristive device can be used to perform low-power write operations for the learning and implement short-term association using temporal coding and spike-timing-dependent plasticity-based learning. An electronic circuit was built to validate the proposed learning scheme with packaged devices, with good reproducibility despite the complex synaptic-like dynamic of the NOMFET in pulse regime.

We study the electrostatic potential of a molecular wire bridging two metallic electrodes in the limit of weak contacts. With the use of a tight-binding model including a fully three-dimensional treatment of the electrostatics of the molecular junction, the potential is shown to be poorly screened, dropping mostly along the entire molecule. In addition, we observe pronounced Friedel oscillations that can be related to the breaking of electron-hole symmetry. Our results are in semi-quantitative agreement with recent state-of-the-art ab initio calculations and point to the need of a threedimensional treatment to properly capture the behavior of the electrostatic potential. Based on these results, current-voltage curves are calculated within the Landauer formalism. It is shown that Coulomb interaction partially compensates the localization of the charges induced by the electric field and consequently tends to suppress zones of negative differential resistance.

An excitonic method proper to study conjugated oligomers and polymers is described and its applicability tested on the ground and first excited states of trans-polyacetylene, taken as a model. From the Pariser-ParrPople Hamiltonian, we derive an effective Hamiltonian based on a local description of the polymer in term of monomers; the relevant electronic configurations are build on a small number of pertinent local excitations.The intuitive and simple microscopic physical picture given by our model supplement recent results, such as the Rice and Garstein ones. Depending of the parameters, the linear absorption appears dominated by an intense excitonic peak.

We study the dynamic electrical response of a silicon-molecular monolayer-metal junctions and we observe two contributions in the admittance spectroscopy data. These contributions are related to dipolar relaxation and molecular organization in the monolayer in one hand, and the presence of defects at the silicon/molecule interface in the other hand. We propose a small signal equivalent circuit suitable for the simulations of these molecular devices in commercial device simulators. Our results concern monolayers of alkyl chains considered as a model system but can be extended to other molecular monolayers. These results open door to a better control and optimization of molecular devices.Comment: 1 pdf file including text, figures and tables. Phys. Rev. B, in pres

Gold electrodes have been deposited by transfer printing (TP) on a thiol-functionalized self-assembled monolayer (SAM) on silicon substrate. A sequential chemical route is reported to incorporate thiol groups (-SH) on a preformed long alkyl chain SAM on silicon. The structural characterizations of the functionalized surfaces are described and confirm success of each chemical step (overall yield of 20%). The electrical measurements of the silicon-molecules-gold TP junctions are compared with junctions made by a classical vacuum evaporation of gold. The temperature-dependent electrical measurements show that the silicon/alkyl/ Au TP junctions exhibit a purely temperature-independent tunnel behavior, while a slight temperature-dependent behavior is observed at a low bias (|V| < 0.5 V) for the junctions with evaporated Au electrodes. Admittance spectroscopy measurements confirm the better dielectric behavior of TP junctions.

Emerging nanocomponents are of great interest to provide adaptability, high density and robustness for the development of new bio-inspired circuits or systems. Although CMOS Neuromorphic circuit was one of the most intense researches to bring the adaptability and robustness in the circuit beyond the conventional Von Neumann architecture in early 1990', CMOS technology could not provide the huge capacity to be scalable to biological levels because a great number of transistors are required to emulate the dynamical behaviors of biological synapse [1]. Nanoscale components are therefore of great interest to develop new neuromorphic circuits by replacing CMOS technology based synapse. The NanoparticleOrganic Memory transistor (NOMFET) is one of the most promising candidates as it can exhibit dynamical behaviors similar to a biological synapse [2]. It could be very suitable to implement some natural synaptic learning mechanisms such as Spike-Time-Dependent-The NOMFET is composed of three terminals as the conventional MOSFET, Drain (D), Source (S) and Gate (G). The device is fabricated using a bottom-gate electrode configuration. Gold nanoparticles (NP) are immobilized on the surface of the inter-electrode gap before pentacene deposition. The conduction in the device is assured by holes, created in the thin film at the interface with the silicon oxide when a negative gate voltage is applied. In addition to rather classical transistor behavior, a negative gate voltage also positively charges the Au NPs. This has the effect of diminishing the channel conductivity of the device, because the charged NPs cause a repulsive electrostatic interaction between the holes trapped in the NPs and the ones created in the pentacene. The NOMFET therefore exhibits a short term memory and the charge retention time in the NPs can be as high as several thousand seconds [4].A functional model is very useful for the design of hybrid Nano/CMOS circuit and architecture as it provides the interface between the fundamental physics and the electrical behaviors. We established the functional model of NOMFET for a two-terminal device configuration, as shown Fig. 1. The gate and the drain electrodes are driven by the same input voltage, a pulse train of a variable frequency. In this configuration, the dynamical behaviors of the NOMFET is a lot as a biological synapse: there is a competition between the charges provided to the NPs by the gate voltage pulses (resulting in a decrease of the drain-to-source current) and the natural NPs charge relaxation (which increases the current intensity until the NPs are fully discharged). When a new input pulse sequence occurs, the NOMFET exhibits either a depressing or a facilitating behavior, depending both on the duration between the pulses and on the charge level of the NPs. The current in the device is thus dependent on the history of the input signal; the maximum variation on our devices is close to 25% with 30V pulses amplitude.Our model is iterative: the drain-to-source current response to a voltage pulse...